Research within the agricultural community has shown that management of crop production may be optimized by taking into account spatial variations that often exist within a given farming field. For example, by varying the farming inputs being applied to a field according to local conditions within the field, a farmer can optimize crop yield as a function of the inputs applied while preventing or minimizing environmental damage. This management technique has become known as precision, site-specific, prescription or spatially-variable farming.
The management of a field using precision farming techniques requires the gathering and processing of data relating to site-specific characteristics of the field. Generally, site-specific input data is analyzed in real-time or off-line to generate a prescription map including desired application rates of a farming input. A control system reads data from the prescription map and generates a control signal which is applied to a variable-rate controller adapted to apply a farming input to the field at a rate that varies as a function of the location. Variable-rate controllers may be mounted on or trailed by agricultural vehicles with attached variable-rate applicators, and may be used to control application rates for applying seed, fertilizer, insecticide, herbicide or other farming inputs. The effect of the inputs may be analyzed by gathering site-specific yield and moisture content data and correlating this data with the farming inputs, thereby allowing a user to optimize the amounts and combinations of farming inputs applied to the field.
The spatially-variable characteristic data may be obtained or measured by manual measuring, remote sensing, or sensing during field operations. Manual measurement typically involve taking a soil probe and analyzing the soil in a laboratory to determine nutrient data or soil condition data such as soil type or soil classification. Taking manual measurements, however, is labor intensive and, due to high sampling costs, provides only a limited number of data samples. Remote sensing may include taking aerial photographs or generating spectral images or maps from airborne or spaceborne multispectral sensors. Spectral data from remote sensing, however, is often difficult to correlate or register with a precise position in a field or with a specific quantifiable characteristic of the field. Both manual measurements and remote sensing require a user to conduct an airborne or ground-based survey of the field apart from normal field operations.
Spatially-variable characteristic data may also be acquired during normal field operations using appropriate sensors supported by a combine, tractor or other vehicle. A variety of characteristics may be sensed including soil properties (e.g., organic matter, fertility, nutrients, moisture content, compaction and topography or altitude), crop properties (e.g., height, moisture content or yield), and farming inputs applied to the field (e.g., fertilizers, herbicides, insecticides, seeds, cultural practices or tillage techniques used). Other spatially-variable characteristics may be manually sensed as a field is traversed (e.g., insect or weed infestation or landmarks). As these examples show, characteristics which correlate to a specific location include data related to local conditions of the field, farming inputs applied to the field, and crops harvested from the field.
Obtaining characteristic data during normal field operations is advantageous over manual measuring and remote sensing in that less labor is required, more data samples may be taken, samples may be easier to correlate with specific positions and separate field scouting trips are not required.
One important type of data which must be gathered in a site-specific farming system includes site-specific quantity or yield data for the crop that is being harvested. Once gathered, the site-specific yield data is correlated with site-specific data for the farming inputs which were used to produce that crop. The results of this analysis provide information which can then be used to improve the farming operation. Existing crop yield monitors are configured to measure the quantity or yield of bulk crops such as grains. These existing crop yield monitors receive grain flow signals generated by grain flow sensors located within the clean grain path of the harvesting vehicle (i.e., a combine). Existing crop yield monitors, however, have not heretofore been available for monitoring the quantity or yield of non-grain or non-bulk crops harvested by suitable harvesting vehicles. For example, there is currently no crop yield monitor suitable for measuring the quantity or yield of sugar cane billets harvested by a sugar cane harvester, or for measuring the yield of other non-grain or non-bulk crops such as potatoes, sugar beets, and other vegetables.
Thus, there is a need for a crop yield monitor which can accurately determine the quantity or yield of non-grain or non-bulk crops harvested by suitable harvesting vehicles. There is also a need for such a non-grain or non-bulk crop yield monitor which can gather site-specific quantity or yield data for use in a site-specific farming system. There is a particular need for a sugar cane yield monitor for use in monitoring the quantity or yield of sugar cane billets harvested by a sugar cane harvester. Other non-grain or non-bulk crops which could be monitored by such a crop yield monitor include potatoes, sugar beets, and other vegetables.